Overview

Dataset statistics

Number of variables27
Number of observations9376
Missing cells115
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory224.0 B

Variable types

Categorical14
Numeric13

Alerts

Destination has constant value ""Constant
To Area has constant value ""Constant
Flight Date has a high cardinality: 776 distinct valuesHigh cardinality
Flight Code has a high cardinality: 180 distinct valuesHigh cardinality
dpt has a high cardinality: 211 distinct valuesHigh cardinality
dpt1 has a high cardinality: 211 distinct valuesHigh cardinality
day_convert has a high cardinality: 776 distinct valuesHigh cardinality
Profit has 111 (1.2%) missing valuesMissing
Left has 6943 (74.1%) zerosZeros
Sold1 has 161 (1.7%) zerosZeros
Left1 has 7261 (77.4%) zerosZeros
Occ.(%)1 has 170 (1.8%) zerosZeros

Reproduction

Analysis started2023-02-14 11:21:24.440990
Analysis finished2023-02-14 11:21:41.927960
Duration17.49 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Destination
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
Turkey
9376 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters56256
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTurkey
2nd rowTurkey
3rd rowTurkey
4th rowTurkey
5th rowTurkey

Common Values

ValueCountFrequency (%)
Turkey 9376
100.0%

Length

2023-02-14T14:21:41.969077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:21:42.053825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
turkey 9376
100.0%

Most occurring characters

ValueCountFrequency (%)
T 9376
16.7%
u 9376
16.7%
r 9376
16.7%
k 9376
16.7%
e 9376
16.7%
y 9376
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46880
83.3%
Uppercase Letter 9376
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 9376
20.0%
r 9376
20.0%
k 9376
20.0%
e 9376
20.0%
y 9376
20.0%
Uppercase Letter
ValueCountFrequency (%)
T 9376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56256
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 9376
16.7%
u 9376
16.7%
r 9376
16.7%
k 9376
16.7%
e 9376
16.7%
y 9376
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 9376
16.7%
u 9376
16.7%
r 9376
16.7%
k 9376
16.7%
e 9376
16.7%
y 9376
16.7%

Origin
Categorical

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
Moscow
5443 
S.Petersburg
1275 
Kazan
637 
Mineralnye Vodi
 
266
Chelyabinsk
 
211
Other values (22)
1544 

Length

Max length15
Median length6
Mean length7.4744027
Min length3

Characters and Unicode

Total characters70080
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBelgorod
2nd rowBelgorod
3rd rowChelyabinsk
4th rowChelyabinsk
5th rowChelyabinsk

Common Values

ValueCountFrequency (%)
Moscow 5443
58.1%
S.Petersburg 1275
 
13.6%
Kazan 637
 
6.8%
Mineralnye Vodi 266
 
2.8%
Chelyabinsk 211
 
2.3%
Samara 191
 
2.0%
Perm 183
 
2.0%
Ekaterinburg 182
 
1.9%
Kaliningrad 178
 
1.9%
Rostov-na-Donu 137
 
1.5%
Other values (17) 673
 
7.2%

Length

2023-02-14T14:21:42.135696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moscow 5443
56.5%
s.petersburg 1275
 
13.2%
kazan 637
 
6.6%
mineralnye 266
 
2.8%
vodi 266
 
2.8%
chelyabinsk 211
 
2.2%
samara 191
 
2.0%
perm 183
 
1.9%
ekaterinburg 182
 
1.9%
kaliningrad 178
 
1.8%
Other values (18) 810
 
8.4%

Most occurring characters

ValueCountFrequency (%)
o 12239
17.5%
s 7301
 
10.4%
M 5709
 
8.1%
c 5560
 
7.9%
w 5443
 
7.8%
r 4138
 
5.9%
e 3856
 
5.5%
a 3320
 
4.7%
n 2399
 
3.4%
g 1817
 
2.6%
Other values (31) 18298
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57089
81.5%
Uppercase Letter 11115
 
15.9%
Other Punctuation 1336
 
1.9%
Dash Punctuation 274
 
0.4%
Space Separator 266
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 12239
21.4%
s 7301
12.8%
c 5560
9.7%
w 5443
9.5%
r 4138
 
7.2%
e 3856
 
6.8%
a 3320
 
5.8%
n 2399
 
4.2%
g 1817
 
3.2%
b 1745
 
3.1%
Other values (13) 9271
16.2%
Uppercase Letter
ValueCountFrequency (%)
M 5709
51.4%
S 1622
 
14.6%
P 1458
 
13.1%
K 859
 
7.7%
V 317
 
2.9%
C 211
 
1.9%
N 185
 
1.7%
E 182
 
1.6%
D 137
 
1.2%
R 137
 
1.2%
Other values (5) 298
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 1336
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 274
100.0%
Space Separator
ValueCountFrequency (%)
266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68204
97.3%
Common 1876
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 12239
17.9%
s 7301
10.7%
M 5709
 
8.4%
c 5560
 
8.2%
w 5443
 
8.0%
r 4138
 
6.1%
e 3856
 
5.7%
a 3320
 
4.9%
n 2399
 
3.5%
g 1817
 
2.7%
Other values (28) 16422
24.1%
Common
ValueCountFrequency (%)
. 1336
71.2%
- 274
 
14.6%
266
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 12239
17.5%
s 7301
 
10.4%
M 5709
 
8.1%
c 5560
 
7.9%
w 5443
 
7.8%
r 4138
 
5.9%
e 3856
 
5.5%
a 3320
 
4.7%
n 2399
 
3.4%
g 1817
 
2.6%
Other values (31) 18298
26.1%

To Area
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
Antalya
9376 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters65632
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAntalya
2nd rowAntalya
3rd rowAntalya
4th rowAntalya
5th rowAntalya

Common Values

ValueCountFrequency (%)
Antalya 9376
100.0%

Length

2023-02-14T14:21:42.198554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:21:42.253566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
antalya 9376
100.0%

Most occurring characters

ValueCountFrequency (%)
a 18752
28.6%
A 9376
14.3%
n 9376
14.3%
t 9376
14.3%
l 9376
14.3%
y 9376
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56256
85.7%
Uppercase Letter 9376
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18752
33.3%
n 9376
16.7%
t 9376
16.7%
l 9376
16.7%
y 9376
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 9376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65632
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18752
28.6%
A 9376
14.3%
n 9376
14.3%
t 9376
14.3%
l 9376
14.3%
y 9376
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18752
28.6%
A 9376
14.3%
n 9376
14.3%
t 9376
14.3%
l 9376
14.3%
y 9376
14.3%

Flight Date
Categorical

Distinct776
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
06.10.2022
 
38
18.09.2022
 
38
26.09.2022
 
38
29.09.2022
 
38
30.09.2022
 
38
Other values (771)
9186 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters93760
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)1.6%

Sample

1st row02.01.2020
2nd row09.01.2020
3rd row02.01.2020
4th row10.01.2020
5th row15.03.2020

Common Values

ValueCountFrequency (%)
06.10.2022 38
 
0.4%
18.09.2022 38
 
0.4%
26.09.2022 38
 
0.4%
29.09.2022 38
 
0.4%
30.09.2022 38
 
0.4%
07.10.2022 38
 
0.4%
13.10.2022 38
 
0.4%
22.09.2022 38
 
0.4%
19.09.2022 38
 
0.4%
25.09.2022 38
 
0.4%
Other values (766) 8996
95.9%

Length

2023-02-14T14:21:42.303428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06.10.2022 38
 
0.4%
13.10.2022 38
 
0.4%
15.09.2022 38
 
0.4%
25.09.2022 38
 
0.4%
19.09.2022 38
 
0.4%
22.09.2022 38
 
0.4%
18.09.2022 38
 
0.4%
07.10.2022 38
 
0.4%
30.09.2022 38
 
0.4%
29.09.2022 38
 
0.4%
Other values (766) 8996
95.9%

Most occurring characters

ValueCountFrequency (%)
2 29459
31.4%
0 22803
24.3%
. 18752
20.0%
1 9031
 
9.6%
9 2737
 
2.9%
8 2592
 
2.8%
7 2362
 
2.5%
6 1914
 
2.0%
5 1547
 
1.6%
3 1511
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75008
80.0%
Other Punctuation 18752
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29459
39.3%
0 22803
30.4%
1 9031
 
12.0%
9 2737
 
3.6%
8 2592
 
3.5%
7 2362
 
3.1%
6 1914
 
2.6%
5 1547
 
2.1%
3 1511
 
2.0%
4 1052
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 18752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29459
31.4%
0 22803
24.3%
. 18752
20.0%
1 9031
 
9.6%
9 2737
 
2.9%
8 2592
 
2.8%
7 2362
 
2.5%
6 1914
 
2.0%
5 1547
 
1.6%
3 1511
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29459
31.4%
0 22803
24.3%
. 18752
20.0%
1 9031
 
9.6%
9 2737
 
2.9%
8 2592
 
2.8%
7 2362
 
2.5%
6 1914
 
2.0%
5 1547
 
1.6%
3 1511
 
1.6%

day_name
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
Saturday
1445 
Sunday
1423 
Thursday
1319 
Friday
1312 
Monday
1305 
Other values (2)
2572 

Length

Max length9
Median length8
Mean length7.1399317
Min length6

Characters and Unicode

Total characters66944
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowFriday
5th rowSunday

Common Values

ValueCountFrequency (%)
Saturday 1445
15.4%
Sunday 1423
15.2%
Thursday 1319
14.1%
Friday 1312
14.0%
Monday 1305
13.9%
Wednesday 1294
13.8%
Tuesday 1278
13.6%

Length

2023-02-14T14:21:42.364424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:21:42.442276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
saturday 1445
15.4%
sunday 1423
15.2%
thursday 1319
14.1%
friday 1312
14.0%
monday 1305
13.9%
wednesday 1294
13.8%
tuesday 1278
13.6%

Most occurring characters

ValueCountFrequency (%)
a 10821
16.2%
d 10670
15.9%
y 9376
14.0%
u 5465
8.2%
r 4076
 
6.1%
n 4022
 
6.0%
s 3891
 
5.8%
e 3866
 
5.8%
S 2868
 
4.3%
T 2597
 
3.9%
Other values (7) 9292
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57568
86.0%
Uppercase Letter 9376
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10821
18.8%
d 10670
18.5%
y 9376
16.3%
u 5465
9.5%
r 4076
 
7.1%
n 4022
 
7.0%
s 3891
 
6.8%
e 3866
 
6.7%
t 1445
 
2.5%
h 1319
 
2.3%
Other values (2) 2617
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
S 2868
30.6%
T 2597
27.7%
F 1312
14.0%
M 1305
13.9%
W 1294
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 66944
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10821
16.2%
d 10670
15.9%
y 9376
14.0%
u 5465
8.2%
r 4076
 
6.1%
n 4022
 
6.0%
s 3891
 
5.8%
e 3866
 
5.8%
S 2868
 
4.3%
T 2597
 
3.9%
Other values (7) 9292
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10821
16.2%
d 10670
15.9%
y 9376
14.0%
u 5465
8.2%
r 4076
 
6.1%
n 4022
 
6.0%
s 3891
 
5.8%
e 3866
 
5.8%
S 2868
 
4.3%
T 2597
 
3.9%
Other values (7) 9292
13.9%

flight_month
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
September
1813 
October
1733 
August
1673 
July
1427 
June
998 
Other values (7)
1732 

Length

Max length9
Median length7
Mean length6.1877133
Min length3

Characters and Unicode

Total characters58016
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJanuary
4th rowJanuary
5th rowMarch

Common Values

ValueCountFrequency (%)
September 1813
19.3%
October 1733
18.5%
August 1673
17.8%
July 1427
15.2%
June 998
10.6%
May 637
 
6.8%
November 469
 
5.0%
December 180
 
1.9%
April 134
 
1.4%
January 133
 
1.4%
Other values (2) 179
 
1.9%

Length

2023-02-14T14:21:42.512130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
september 1813
19.3%
october 1733
18.5%
august 1673
17.8%
july 1427
15.2%
june 998
10.6%
may 637
 
6.8%
november 469
 
5.0%
december 180
 
1.9%
april 134
 
1.4%
january 133
 
1.4%
Other values (2) 179
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 9725
16.8%
u 5981
 
10.3%
t 5219
 
9.0%
r 4718
 
8.1%
b 4272
 
7.4%
J 2558
 
4.4%
m 2462
 
4.2%
y 2274
 
3.9%
o 2202
 
3.8%
c 2015
 
3.5%
Other values (16) 16590
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48640
83.8%
Uppercase Letter 9376
 
16.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9725
20.0%
u 5981
12.3%
t 5219
10.7%
r 4718
9.7%
b 4272
8.8%
m 2462
 
5.1%
y 2274
 
4.7%
o 2202
 
4.5%
c 2015
 
4.1%
p 1947
 
4.0%
Other values (8) 7825
16.1%
Uppercase Letter
ValueCountFrequency (%)
J 2558
27.3%
S 1813
19.3%
A 1807
19.3%
O 1733
18.5%
M 739
 
7.9%
N 469
 
5.0%
D 180
 
1.9%
F 77
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 58016
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9725
16.8%
u 5981
 
10.3%
t 5219
 
9.0%
r 4718
 
8.1%
b 4272
 
7.4%
J 2558
 
4.4%
m 2462
 
4.2%
y 2274
 
3.9%
o 2202
 
3.8%
c 2015
 
3.5%
Other values (16) 16590
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9725
16.8%
u 5981
 
10.3%
t 5219
 
9.0%
r 4718
 
8.1%
b 4272
 
7.4%
J 2558
 
4.4%
m 2462
 
4.2%
y 2274
 
3.9%
o 2202
 
3.8%
c 2015
 
3.5%
Other values (16) 16590
28.6%

season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
Summer
4098 
Fall
4015 
Spring
873 
Winter
 
390

Length

Max length6
Median length6
Mean length5.143558
Min length4

Characters and Unicode

Total characters48226
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowSpring

Common Values

ValueCountFrequency (%)
Summer 4098
43.7%
Fall 4015
42.8%
Spring 873
 
9.3%
Winter 390
 
4.2%

Length

2023-02-14T14:21:42.580859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:21:42.663987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
summer 4098
43.7%
fall 4015
42.8%
spring 873
 
9.3%
winter 390
 
4.2%

Most occurring characters

ValueCountFrequency (%)
m 8196
17.0%
l 8030
16.7%
r 5361
11.1%
S 4971
10.3%
e 4488
9.3%
u 4098
8.5%
F 4015
8.3%
a 4015
8.3%
i 1263
 
2.6%
n 1263
 
2.6%
Other values (4) 2526
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38850
80.6%
Uppercase Letter 9376
 
19.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 8196
21.1%
l 8030
20.7%
r 5361
13.8%
e 4488
11.6%
u 4098
10.5%
a 4015
10.3%
i 1263
 
3.3%
n 1263
 
3.3%
p 873
 
2.2%
g 873
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
S 4971
53.0%
F 4015
42.8%
W 390
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 48226
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 8196
17.0%
l 8030
16.7%
r 5361
11.1%
S 4971
10.3%
e 4488
9.3%
u 4098
8.5%
F 4015
8.3%
a 4015
8.3%
i 1263
 
2.6%
n 1263
 
2.6%
Other values (4) 2526
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 8196
17.0%
l 8030
16.7%
r 5361
11.1%
S 4971
10.3%
e 4488
9.3%
u 4098
8.5%
F 4015
8.3%
a 4015
8.3%
i 1263
 
2.6%
n 1263
 
2.6%
Other values (4) 2526
 
5.2%

year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
2022
6522 
2021
1868 
2020
986 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters37504
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2022 6522
69.6%
2021 1868
 
19.9%
2020 986
 
10.5%

Length

2023-02-14T14:21:42.723831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:21:42.795621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2022 6522
69.6%
2021 1868
 
19.9%
2020 986
 
10.5%

Most occurring characters

ValueCountFrequency (%)
2 25274
67.4%
0 10362
27.6%
1 1868
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37504
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 25274
67.4%
0 10362
27.6%
1 1868
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37504
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 25274
67.4%
0 10362
27.6%
1 1868
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 25274
67.4%
0 10362
27.6%
1 1868
 
5.0%

Flight Code
Categorical

Distinct180
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
TK 3653
 
404
TK 3655
 
384
TK 212
 
381
TK 3657
 
348
RL 7709
 
294
Other values (175)
7565 

Length

Max length12
Median length7
Mean length6.9731229
Min length6

Characters and Unicode

Total characters65380
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.2%

Sample

1st rowWZ 4035.
2nd rowWZ 4035.
3rd rowU6 1009
4th rowU6 1009
5th rowWZ 4009

Common Values

ValueCountFrequency (%)
TK 3653 404
 
4.3%
TK 3655 384
 
4.1%
TK 212 381
 
4.1%
TK 3657 348
 
3.7%
RL 7709 294
 
3.1%
PC 1581 250
 
2.7%
RL 7711 241
 
2.6%
TK 3961 229
 
2.4%
RL 7701 200
 
2.1%
U6 3001 195
 
2.1%
Other values (170) 6450
68.8%

Length

2023-02-14T14:21:42.851625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tk 4928
26.3%
rl 1492
 
8.0%
zf 942
 
5.0%
wz 564
 
3.0%
u6 557
 
3.0%
pc 415
 
2.2%
3653 404
 
2.2%
3655 384
 
2.0%
212 381
 
2.0%
3657 348
 
1.9%
Other values (176) 8337
44.5%

Most occurring characters

ValueCountFrequency (%)
9376
14.3%
3 7585
11.6%
1 5969
9.1%
7 5143
 
7.9%
T 4928
 
7.5%
K 4928
 
7.5%
5 4295
 
6.6%
6 3889
 
5.9%
9 3172
 
4.9%
0 2981
 
4.6%
Other values (18) 13114
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37682
57.6%
Uppercase Letter 18039
27.6%
Space Separator 9376
 
14.3%
Other Punctuation 283
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 4928
27.3%
K 4928
27.3%
Z 1506
 
8.3%
R 1492
 
8.3%
L 1492
 
8.3%
F 942
 
5.2%
U 720
 
4.0%
W 564
 
3.1%
C 416
 
2.3%
P 415
 
2.3%
Other values (5) 636
 
3.5%
Decimal Number
ValueCountFrequency (%)
3 7585
20.1%
1 5969
15.8%
7 5143
13.6%
5 4295
11.4%
6 3889
10.3%
9 3172
8.4%
0 2981
 
7.9%
2 1638
 
4.3%
4 1515
 
4.0%
8 1495
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 270
95.4%
/ 13
 
4.6%
Space Separator
ValueCountFrequency (%)
9376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47341
72.4%
Latin 18039
 
27.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 4928
27.3%
K 4928
27.3%
Z 1506
 
8.3%
R 1492
 
8.3%
L 1492
 
8.3%
F 942
 
5.2%
U 720
 
4.0%
W 564
 
3.1%
C 416
 
2.3%
P 415
 
2.3%
Other values (5) 636
 
3.5%
Common
ValueCountFrequency (%)
9376
19.8%
3 7585
16.0%
1 5969
12.6%
7 5143
10.9%
5 4295
9.1%
6 3889
8.2%
9 3172
 
6.7%
0 2981
 
6.3%
2 1638
 
3.5%
4 1515
 
3.2%
Other values (3) 1778
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9376
14.3%
3 7585
11.6%
1 5969
9.1%
7 5143
 
7.9%
T 4928
 
7.5%
K 4928
 
7.5%
5 4295
 
6.6%
6 3889
 
5.9%
9 3172
 
4.9%
0 2981
 
4.6%
Other values (18) 13114
20.1%

Days
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0752986
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size146.5 KiB
2023-02-14T14:21:42.914532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0132054
Coefficient of variation (CV)0.49400194
Kurtosis-1.2656533
Mean4.0752986
Median Absolute Deviation (MAD)2
Skewness-0.052920757
Sum38210
Variance4.0529962
MonotonicityNot monotonic
2023-02-14T14:21:42.965556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1445
15.4%
7 1423
15.2%
4 1319
14.1%
5 1312
14.0%
1 1305
13.9%
3 1294
13.8%
2 1278
13.6%
ValueCountFrequency (%)
1 1305
13.9%
2 1278
13.6%
3 1294
13.8%
4 1319
14.1%
5 1312
14.0%
6 1445
15.4%
7 1423
15.2%
ValueCountFrequency (%)
7 1423
15.2%
6 1445
15.4%
5 1312
14.0%
4 1319
14.1%
3 1294
13.8%
2 1278
13.6%
1 1305
13.9%

Airline Company
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
Turkish Airlines
4928 
Royal Flight
1492 
Azur Air
942 
Red Wings Airlines
564 
Ural Airlines
557 
Other values (6)
893 

Length

Max length18
Median length16
Mean length14.127133
Min length5

Characters and Unicode

Total characters132456
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRed Wings Airlines
2nd rowRed Wings Airlines
3rd rowUral Airlines
4th rowUral Airlines
5th rowRed Wings Airlines

Common Values

ValueCountFrequency (%)
Turkish Airlines 4928
52.6%
Royal Flight 1492
 
15.9%
Azur Air 942
 
10.0%
Red Wings Airlines 564
 
6.0%
Ural Airlines 557
 
5.9%
Pegasus Airlines 415
 
4.4%
Aeroflot 163
 
1.7%
Pegas Fly 158
 
1.7%
Southwind 95
 
1.0%
Nord Wind 61
 
0.7%

Length

2023-02-14T14:21:43.041450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
airlines 6464
33.9%
turkish 4928
25.9%
royal 1492
 
7.8%
flight 1492
 
7.8%
azur 942
 
4.9%
air 942
 
4.9%
red 564
 
3.0%
wings 564
 
3.0%
ural 557
 
2.9%
pegasus 415
 
2.2%
Other values (7) 697
 
3.7%

Most occurring characters

ValueCountFrequency (%)
i 21010
15.9%
r 14057
10.6%
s 12944
9.8%
l 10327
 
7.8%
9681
 
7.3%
A 8511
 
6.4%
e 7764
 
5.9%
n 7184
 
5.4%
h 6515
 
4.9%
u 6380
 
4.8%
Other values (20) 28083
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 103718
78.3%
Uppercase Letter 19057
 
14.4%
Space Separator 9681
 
7.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 21010
20.3%
r 14057
13.6%
s 12944
12.5%
l 10327
10.0%
e 7764
 
7.5%
n 7184
 
6.9%
h 6515
 
6.3%
u 6380
 
6.2%
k 4928
 
4.8%
g 2629
 
2.5%
Other values (9) 9980
9.6%
Uppercase Letter
ValueCountFrequency (%)
A 8511
44.7%
T 4928
25.9%
R 2056
 
10.8%
F 1650
 
8.7%
W 625
 
3.3%
P 573
 
3.0%
U 557
 
2.9%
S 95
 
0.5%
N 61
 
0.3%
Y 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
9681
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 122775
92.7%
Common 9681
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 21010
17.1%
r 14057
11.4%
s 12944
10.5%
l 10327
8.4%
A 8511
 
6.9%
e 7764
 
6.3%
n 7184
 
5.9%
h 6515
 
5.3%
u 6380
 
5.2%
T 4928
 
4.0%
Other values (19) 23155
18.9%
Common
ValueCountFrequency (%)
9681
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 21010
15.9%
r 14057
10.6%
s 12944
9.8%
l 10327
 
7.8%
9681
 
7.3%
A 8511
 
6.4%
e 7764
 
5.9%
n 7184
 
5.4%
h 6515
 
4.9%
u 6380
 
4.8%
Other values (20) 28083
21.2%

dpt
Categorical

Distinct211
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
02:45:00
 
515
02:30:00
 
499
03:45:00
 
475
night
 
438
14:20:00
 
387
Other values (206)
7062 

Length

Max length9
Median length8
Mean length7.8630546
Min length5

Characters and Unicode

Total characters73724
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)0.6%

Sample

1st rowafternoon
2nd rowafternoon
3rd rownight
4th rownight
5th rowafternoon

Common Values

ValueCountFrequency (%)
02:45:00 515
 
5.5%
02:30:00 499
 
5.3%
03:45:00 475
 
5.1%
night 438
 
4.7%
14:20:00 387
 
4.1%
02:25:00 344
 
3.7%
13:35:00 306
 
3.3%
afternoon 289
 
3.1%
12:55:00 263
 
2.8%
morning 256
 
2.7%
Other values (201) 5604
59.8%

Length

2023-02-14T14:21:43.111321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02:45:00 515
 
5.5%
02:30:00 499
 
5.3%
03:45:00 475
 
5.1%
night 438
 
4.7%
14:20:00 387
 
4.1%
02:25:00 344
 
3.7%
13:35:00 306
 
3.3%
afternoon 289
 
3.1%
12:55:00 263
 
2.8%
morning 256
 
2.7%
Other values (201) 5604
59.8%

Most occurring characters

ValueCountFrequency (%)
0 26201
35.5%
: 16780
22.8%
5 6581
 
8.9%
1 5418
 
7.3%
2 4256
 
5.8%
3 4066
 
5.5%
4 3160
 
4.3%
n 1534
 
2.1%
o 834
 
1.1%
t 727
 
1.0%
Other values (13) 4167
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50340
68.3%
Other Punctuation 16780
 
22.8%
Lowercase Letter 6604
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1534
23.2%
o 834
12.6%
t 727
11.0%
i 697
10.6%
g 697
10.6%
r 545
 
8.3%
h 438
 
6.6%
e 295
 
4.5%
f 289
 
4.4%
a 289
 
4.4%
Other values (2) 259
 
3.9%
Decimal Number
ValueCountFrequency (%)
0 26201
52.0%
5 6581
 
13.1%
1 5418
 
10.8%
2 4256
 
8.5%
3 4066
 
8.1%
4 3160
 
6.3%
8 239
 
0.5%
6 204
 
0.4%
7 128
 
0.3%
9 87
 
0.2%
Other Punctuation
ValueCountFrequency (%)
: 16780
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67120
91.0%
Latin 6604
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1534
23.2%
o 834
12.6%
t 727
11.0%
i 697
10.6%
g 697
10.6%
r 545
 
8.3%
h 438
 
6.6%
e 295
 
4.5%
f 289
 
4.4%
a 289
 
4.4%
Other values (2) 259
 
3.9%
Common
ValueCountFrequency (%)
0 26201
39.0%
: 16780
25.0%
5 6581
 
9.8%
1 5418
 
8.1%
2 4256
 
6.3%
3 4066
 
6.1%
4 3160
 
4.7%
8 239
 
0.4%
6 204
 
0.3%
7 128
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26201
35.5%
: 16780
22.8%
5 6581
 
8.9%
1 5418
 
7.3%
2 4256
 
5.8%
3 4066
 
5.5%
4 3160
 
4.3%
n 1534
 
2.1%
o 834
 
1.1%
t 727
 
1.0%
Other values (13) 4167
 
5.7%

Block
Real number (ℝ)

Distinct131
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.07029
Minimum1
Maximum492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size146.5 KiB
2023-02-14T14:21:43.209869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q134
median63
Q3186
95-th percentile478
Maximum492
Range491
Interquartile range (IQR)152

Descriptive statistics

Standard deviation115.04793
Coefficient of variation (CV)1.0452224
Kurtosis3.1388998
Mean110.07029
Median Absolute Deviation (MAD)43
Skewness1.8293138
Sum1032019
Variance13236.025
MonotonicityNot monotonic
2023-02-14T14:21:43.327785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 797
 
8.5%
30 630
 
6.7%
64 528
 
5.6%
65 464
 
4.9%
16 394
 
4.2%
163 386
 
4.1%
62 352
 
3.8%
193 341
 
3.6%
112 310
 
3.3%
34 274
 
2.9%
Other values (121) 4900
52.3%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 6
 
0.1%
3 4
 
< 0.1%
4 107
1.1%
5 255
2.7%
6 7
 
0.1%
7 13
 
0.1%
8 17
 
0.2%
9 16
 
0.2%
10 34
 
0.4%
ValueCountFrequency (%)
492 8
 
0.1%
490 6
 
0.1%
489 3
 
< 0.1%
488 7
 
0.1%
487 9
 
0.1%
486 6
 
0.1%
485 11
 
0.1%
484 14
0.1%
483 17
0.2%
482 33
0.4%

Sold
Real number (ℝ)

Distinct347
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.84642
Minimum0
Maximum492
Zeros67
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size146.5 KiB
2023-02-14T14:21:43.402610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q130
median62
Q3163
95-th percentile330
Maximum492
Range492
Interquartile range (IQR)133

Descriptive statistics

Standard deviation108.69047
Coefficient of variation (CV)1.0777821
Kurtosis4.0346579
Mean100.84642
Median Absolute Deviation (MAD)42
Skewness2.0024357
Sum945536
Variance11813.617
MonotonicityNot monotonic
2023-02-14T14:21:43.474723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 498
 
5.3%
64 456
 
4.9%
65 403
 
4.3%
16 345
 
3.7%
163 331
 
3.5%
62 310
 
3.3%
193 291
 
3.1%
34 268
 
2.9%
112 266
 
2.8%
61 259
 
2.8%
Other values (337) 5949
63.4%
ValueCountFrequency (%)
0 67
 
0.7%
1 14
 
0.1%
2 27
 
0.3%
3 34
 
0.4%
4 92
 
1.0%
5 255
2.7%
6 26
 
0.3%
7 30
 
0.3%
8 32
 
0.3%
9 43
 
0.5%
ValueCountFrequency (%)
492 2
 
< 0.1%
490 2
 
< 0.1%
489 2
 
< 0.1%
488 3
 
< 0.1%
487 8
0.1%
486 4
 
< 0.1%
485 7
0.1%
484 7
0.1%
483 11
0.1%
482 17
0.2%

Left
Real number (ℝ)

Distinct239
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2238695
Minimum-4
Maximum478
Zeros6943
Zeros (%)74.1%
Negative12
Negative (%)0.1%
Memory size146.5 KiB
2023-02-14T14:21:43.548428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile0
Q10
median0
Q31
95-th percentile54
Maximum478
Range482
Interquartile range (IQR)1

Descriptive statistics

Standard deviation36.290315
Coefficient of variation (CV)3.9343917
Kurtosis48.267972
Mean9.2238695
Median Absolute Deviation (MAD)0
Skewness6.1889251
Sum86483
Variance1316.987
MonotonicityNot monotonic
2023-02-14T14:21:43.631326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6943
74.1%
1 441
 
4.7%
2 359
 
3.8%
3 175
 
1.9%
4 137
 
1.5%
5 76
 
0.8%
6 57
 
0.6%
8 50
 
0.5%
7 48
 
0.5%
10 40
 
0.4%
Other values (229) 1050
 
11.2%
ValueCountFrequency (%)
-4 1
 
< 0.1%
-3 1
 
< 0.1%
-2 5
 
0.1%
-1 5
 
0.1%
0 6943
74.1%
1 441
 
4.7%
2 359
 
3.8%
3 175
 
1.9%
4 137
 
1.5%
5 76
 
0.8%
ValueCountFrequency (%)
478 4
< 0.1%
476 1
 
< 0.1%
443 1
 
< 0.1%
406 1
 
< 0.1%
400 1
 
< 0.1%
395 1
 
< 0.1%
385 1
 
< 0.1%
376 1
 
< 0.1%
374 1
 
< 0.1%
371 1
 
< 0.1%

Occ.(%)
Real number (ℝ)

Distinct104
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.901877
Minimum0
Maximum103
Zeros68
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size146.5 KiB
2023-02-14T14:21:43.713187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q1100
median100
Q3100
95-th percentile100
Maximum103
Range103
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.139012
Coefficient of variation (CV)0.19316986
Kurtosis11.976474
Mean93.901877
Median Absolute Deviation (MAD)0
Skewness-3.50458
Sum880424
Variance329.02376
MonotonicityNot monotonic
2023-02-14T14:21:43.789006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7172
76.5%
99 357
 
3.8%
98 218
 
2.3%
97 162
 
1.7%
93 98
 
1.0%
0 68
 
0.7%
96 57
 
0.6%
94 49
 
0.5%
95 47
 
0.5%
80 37
 
0.4%
Other values (94) 1111
 
11.8%
ValueCountFrequency (%)
0 68
0.7%
1 1
 
< 0.1%
2 4
 
< 0.1%
3 6
 
0.1%
4 4
 
< 0.1%
5 7
 
0.1%
6 4
 
< 0.1%
7 8
 
0.1%
8 1
 
< 0.1%
9 6
 
0.1%
ValueCountFrequency (%)
103 1
 
< 0.1%
102 1
 
< 0.1%
101 4
 
< 0.1%
100 7172
76.5%
99 357
 
3.8%
98 218
 
2.3%
97 162
 
1.7%
96 57
 
0.6%
95 47
 
0.5%
94 49
 
0.5%

dpt1
Categorical

Distinct211
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
21:40:00
 
445
morning
 
399
08:25:00
 
395
07:25:00
 
394
20:05:00
 
394
Other values (206)
7349 

Length

Max length9
Median length8
Mean length7.9477389
Min length5

Characters and Unicode

Total characters74518
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.3%

Sample

1st rowmorning
2nd rowmorning
3rd rowmorning
4th rowmorning
5th rowmorning

Common Values

ValueCountFrequency (%)
21:40:00 445
 
4.7%
morning 399
 
4.3%
08:25:00 395
 
4.2%
07:25:00 394
 
4.2%
20:05:00 394
 
4.2%
20:30:00 354
 
3.8%
09:40:00 339
 
3.6%
evening 319
 
3.4%
20:50:00 271
 
2.9%
20:55:00 270
 
2.9%
Other values (201) 5796
61.8%

Length

2023-02-14T14:21:43.862902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21:40:00 445
 
4.7%
morning 399
 
4.3%
08:25:00 395
 
4.2%
20:05:00 394
 
4.2%
07:25:00 394
 
4.2%
20:30:00 354
 
3.8%
09:40:00 339
 
3.6%
evening 319
 
3.4%
20:50:00 271
 
2.9%
20:55:00 270
 
2.9%
Other values (201) 5796
61.8%

Most occurring characters

ValueCountFrequency (%)
0 28661
38.5%
: 16780
22.5%
2 6004
 
8.1%
5 5382
 
7.2%
1 3289
 
4.4%
n 1962
 
2.6%
4 1857
 
2.5%
8 1457
 
2.0%
7 1304
 
1.7%
3 1175
 
1.6%
Other values (13) 6647
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50340
67.6%
Other Punctuation 16780
 
22.5%
Lowercase Letter 7398
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1962
26.5%
o 915
12.4%
e 896
12.1%
i 728
 
9.8%
g 728
 
9.8%
r 657
 
8.9%
m 399
 
5.4%
v 319
 
4.3%
t 268
 
3.6%
a 258
 
3.5%
Other values (2) 268
 
3.6%
Decimal Number
ValueCountFrequency (%)
0 28661
56.9%
2 6004
 
11.9%
5 5382
 
10.7%
1 3289
 
6.5%
4 1857
 
3.7%
8 1457
 
2.9%
7 1304
 
2.6%
3 1175
 
2.3%
9 1022
 
2.0%
6 189
 
0.4%
Other Punctuation
ValueCountFrequency (%)
: 16780
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67120
90.1%
Latin 7398
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1962
26.5%
o 915
12.4%
e 896
12.1%
i 728
 
9.8%
g 728
 
9.8%
r 657
 
8.9%
m 399
 
5.4%
v 319
 
4.3%
t 268
 
3.6%
a 258
 
3.5%
Other values (2) 268
 
3.6%
Common
ValueCountFrequency (%)
0 28661
42.7%
: 16780
25.0%
2 6004
 
8.9%
5 5382
 
8.0%
1 3289
 
4.9%
4 1857
 
2.8%
8 1457
 
2.2%
7 1304
 
1.9%
3 1175
 
1.8%
9 1022
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28661
38.5%
: 16780
22.5%
2 6004
 
8.1%
5 5382
 
7.2%
1 3289
 
4.4%
n 1962
 
2.6%
4 1857
 
2.5%
8 1457
 
2.0%
7 1304
 
1.7%
3 1175
 
1.6%
Other values (13) 6647
 
8.9%

Block1
Real number (ℝ)

Distinct136
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.83948
Minimum0
Maximum492
Zeros24
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size146.5 KiB
2023-02-14T14:21:43.952740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q134
median63
Q3186
95-th percentile474
Maximum492
Range492
Interquartile range (IQR)152

Descriptive statistics

Standard deviation114.94723
Coefficient of variation (CV)1.0465019
Kurtosis3.1333777
Mean109.83948
Median Absolute Deviation (MAD)44
Skewness1.824476
Sum1029855
Variance13212.865
MonotonicityNot monotonic
2023-02-14T14:21:44.053595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 795
 
8.5%
30 617
 
6.6%
64 529
 
5.6%
65 461
 
4.9%
163 387
 
4.1%
62 350
 
3.7%
193 342
 
3.6%
112 310
 
3.3%
16 299
 
3.2%
478 292
 
3.1%
Other values (126) 4994
53.3%
ValueCountFrequency (%)
0 24
 
0.3%
1 6
 
0.1%
2 7
 
0.1%
3 15
 
0.2%
4 109
1.2%
5 242
2.6%
6 11
 
0.1%
7 9
 
0.1%
8 10
 
0.1%
9 32
 
0.3%
ValueCountFrequency (%)
492 11
0.1%
491 4
 
< 0.1%
490 5
0.1%
489 5
0.1%
488 7
0.1%
487 7
0.1%
486 6
0.1%
485 6
0.1%
484 7
0.1%
483 8
0.1%

Sold1
Real number (ℝ)

Distinct339
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.65422
Minimum0
Maximum494
Zeros161
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size146.5 KiB
2023-02-14T14:21:44.124455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q130
median62
Q3163
95-th percentile330
Maximum494
Range494
Interquartile range (IQR)133

Descriptive statistics

Standard deviation109.24871
Coefficient of variation (CV)1.0853862
Kurtosis3.8942459
Mean100.65422
Median Absolute Deviation (MAD)43.5
Skewness1.9612844
Sum943734
Variance11935.28
MonotonicityNot monotonic
2023-02-14T14:21:44.231210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 522
 
5.6%
64 502
 
5.4%
65 432
 
4.6%
220 386
 
4.1%
163 326
 
3.5%
193 309
 
3.3%
62 299
 
3.2%
112 259
 
2.8%
34 247
 
2.6%
61 246
 
2.6%
Other values (329) 5848
62.4%
ValueCountFrequency (%)
0 161
1.7%
1 29
 
0.3%
2 35
 
0.4%
3 60
 
0.6%
4 112
1.2%
5 231
2.5%
6 38
 
0.4%
7 24
 
0.3%
8 33
 
0.4%
9 43
 
0.5%
ValueCountFrequency (%)
494 2
 
< 0.1%
493 1
 
< 0.1%
492 2
 
< 0.1%
491 4
< 0.1%
490 4
< 0.1%
489 5
0.1%
488 4
< 0.1%
487 4
< 0.1%
486 2
 
< 0.1%
485 3
< 0.1%

Left1
Real number (ℝ)

Distinct243
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1852602
Minimum-6
Maximum478
Zeros7261
Zeros (%)77.4%
Negative127
Negative (%)1.4%
Memory size146.5 KiB
2023-02-14T14:21:44.380602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile0
Q10
median0
Q30
95-th percentile48
Maximum478
Range484
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.870404
Coefficient of variation (CV)4.449564
Kurtosis57.56316
Mean9.1852602
Median Absolute Deviation (MAD)0
Skewness6.8969319
Sum86121
Variance1670.3899
MonotonicityNot monotonic
2023-02-14T14:21:44.466702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7261
77.4%
1 374
 
4.0%
2 208
 
2.2%
3 120
 
1.3%
4 93
 
1.0%
-1 71
 
0.8%
5 61
 
0.7%
6 60
 
0.6%
7 51
 
0.5%
8 44
 
0.5%
Other values (233) 1033
 
11.0%
ValueCountFrequency (%)
-6 2
 
< 0.1%
-5 1
 
< 0.1%
-4 4
 
< 0.1%
-3 16
 
0.2%
-2 33
 
0.4%
-1 71
 
0.8%
0 7261
77.4%
1 374
 
4.0%
2 208
 
2.2%
3 120
 
1.3%
ValueCountFrequency (%)
478 1
 
< 0.1%
474 6
0.1%
473 3
< 0.1%
472 2
 
< 0.1%
471 1
 
< 0.1%
470 1
 
< 0.1%
468 2
 
< 0.1%
465 1
 
< 0.1%
464 1
 
< 0.1%
457 1
 
< 0.1%

Occ.(%)1
Real number (ℝ)

Distinct105
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.139078
Minimum0
Maximum107
Zeros170
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size146.5 KiB
2023-02-14T14:21:44.544953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.75
Q1100
median100
Q3100
95-th percentile100
Maximum107
Range107
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.796022
Coefficient of variation (CV)0.22327923
Kurtosis10.981702
Mean93.139078
Median Absolute Deviation (MAD)0
Skewness-3.4289362
Sum873272
Variance432.47452
MonotonicityNot monotonic
2023-02-14T14:21:44.621881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7427
79.2%
99 197
 
2.1%
0 170
 
1.8%
98 144
 
1.5%
97 106
 
1.1%
93 87
 
0.9%
101 55
 
0.6%
96 52
 
0.6%
94 47
 
0.5%
75 43
 
0.5%
Other values (95) 1048
 
11.2%
ValueCountFrequency (%)
0 170
1.8%
1 14
 
0.1%
2 15
 
0.2%
3 11
 
0.1%
4 7
 
0.1%
5 9
 
0.1%
6 14
 
0.1%
7 11
 
0.1%
8 2
 
< 0.1%
9 9
 
0.1%
ValueCountFrequency (%)
107 1
 
< 0.1%
103 1
 
< 0.1%
102 7
 
0.1%
101 55
 
0.6%
100 7427
79.2%
99 197
 
2.1%
98 144
 
1.5%
97 106
 
1.1%
96 52
 
0.6%
95 34
 
0.4%

Occ.
Real number (ℝ)

Distinct831
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.893706
Minimum0
Maximum102.94
Zeros67
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size146.5 KiB
2023-02-14T14:21:44.701894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.135
Q199.59
median100
Q3100
95-th percentile100
Maximum102.94
Range102.94
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation18.13997
Coefficient of variation (CV)0.19319687
Kurtosis11.972902
Mean93.893706
Median Absolute Deviation (MAD)0
Skewness-3.504088
Sum880347.39
Variance329.05852
MonotonicityNot monotonic
2023-02-14T14:21:44.786626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 6943
74.1%
99.55 67
 
0.7%
0 67
 
0.7%
99.09 66
 
0.7%
96.67 62
 
0.7%
99.79 59
 
0.6%
93.33 46
 
0.5%
99.58 40
 
0.4%
98.18 34
 
0.4%
98.64 33
 
0.4%
Other values (821) 1959
 
20.9%
ValueCountFrequency (%)
0 67
0.7%
0.45 1
 
< 0.1%
1.36 1
 
< 0.1%
1.72 1
 
< 0.1%
1.79 1
 
< 0.1%
1.82 1
 
< 0.1%
2.27 1
 
< 0.1%
2.73 1
 
< 0.1%
3.17 1
 
< 0.1%
3.33 2
 
< 0.1%
ValueCountFrequency (%)
102.94 1
 
< 0.1%
101.63 1
 
< 0.1%
100.91 2
 
< 0.1%
100.83 1
 
< 0.1%
100.54 1
 
< 0.1%
100.43 1
 
< 0.1%
100.42 3
 
< 0.1%
100.21 2
 
< 0.1%
100 6943
74.1%
99.8 3
 
< 0.1%

Netto
Real number (ℝ)

Distinct746
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean357.15978
Minimum50
Maximum750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size146.5 KiB
2023-02-14T14:21:44.869103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile166
Q1258
median360.245
Q3405.7
95-th percentile660.36
Maximum750
Range700
Interquartile range (IQR)147.7

Descriptive statistics

Standard deviation131.83058
Coefficient of variation (CV)0.36910814
Kurtosis0.84227291
Mean357.15978
Median Absolute Deviation (MAD)64.255
Skewness0.72064999
Sum3348730.1
Variance17379.302
MonotonicityNot monotonic
2023-02-14T14:21:44.944822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
370 437
 
4.7%
369.24 308
 
3.3%
353.24 308
 
3.3%
377.18 293
 
3.1%
375 270
 
2.9%
356.36 252
 
2.7%
353.37 240
 
2.6%
320 236
 
2.5%
400 216
 
2.3%
703.18 168
 
1.8%
Other values (736) 6648
70.9%
ValueCountFrequency (%)
50 1
 
< 0.1%
75 1
 
< 0.1%
80 1
 
< 0.1%
95 1
 
< 0.1%
100 1
 
< 0.1%
124 1
 
< 0.1%
130 2
< 0.1%
131 3
< 0.1%
133 2
< 0.1%
135 3
< 0.1%
ValueCountFrequency (%)
750 80
0.9%
703.18 168
1.8%
700 126
1.3%
693.38 22
 
0.2%
660.36 126
1.3%
657.37 120
1.3%
641 1
 
< 0.1%
625 1
 
< 0.1%
623.52 3
 
< 0.1%
605 1
 
< 0.1%

Netto Currency
Categorical

Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size146.5 KiB
EUR
6386 
USD
2986 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters28116
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUR
2nd rowEUR
3rd rowEUR
4th rowEUR
5th rowEUR

Common Values

ValueCountFrequency (%)
EUR 6386
68.1%
USD 2986
31.8%
(Missing) 4
 
< 0.1%

Length

2023-02-14T14:21:45.050776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-14T14:21:45.104908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
eur 6386
68.1%
usd 2986
31.9%

Most occurring characters

ValueCountFrequency (%)
U 9372
33.3%
E 6386
22.7%
R 6386
22.7%
S 2986
 
10.6%
D 2986
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 28116
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 9372
33.3%
E 6386
22.7%
R 6386
22.7%
S 2986
 
10.6%
D 2986
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 28116
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 9372
33.3%
E 6386
22.7%
R 6386
22.7%
S 2986
 
10.6%
D 2986
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 9372
33.3%
E 6386
22.7%
R 6386
22.7%
S 2986
 
10.6%
D 2986
 
10.6%

Profit
Real number (ℝ)

Distinct8646
Distinct (%)93.3%
Missing111
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean244.88502
Minimum-555.91
Maximum2552.46
Zeros0
Zeros (%)0.0%
Negative1230
Negative (%)13.1%
Memory size146.5 KiB
2023-02-14T14:21:45.161011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-555.91
5-th percentile-40.556
Q155.92
median229.11
Q3397.39
95-th percentile594.254
Maximum2552.46
Range3108.37
Interquartile range (IQR)341.47

Descriptive statistics

Standard deviation215.33367
Coefficient of variation (CV)0.87932562
Kurtosis2.7358277
Mean244.88502
Median Absolute Deviation (MAD)170.45
Skewness0.79512052
Sum2268859.7
Variance46368.59
MonotonicityNot monotonic
2023-02-14T14:21:45.229280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.8 4
 
< 0.1%
19.43 4
 
< 0.1%
347.61 3
 
< 0.1%
161.07 3
 
< 0.1%
4.38 3
 
< 0.1%
41.19 3
 
< 0.1%
-30.56 3
 
< 0.1%
317.27 3
 
< 0.1%
147.54 3
 
< 0.1%
8.81 3
 
< 0.1%
Other values (8636) 9233
98.5%
(Missing) 111
 
1.2%
ValueCountFrequency (%)
-555.91 1
< 0.1%
-329.46 1
< 0.1%
-277.85 1
< 0.1%
-277.21 1
< 0.1%
-262.51 1
< 0.1%
-255.65 1
< 0.1%
-252.83 1
< 0.1%
-235.81 1
< 0.1%
-224.27 1
< 0.1%
-223.38 1
< 0.1%
ValueCountFrequency (%)
2552.46 1
< 0.1%
1921.86 1
< 0.1%
1660.82 1
< 0.1%
1635.96 1
< 0.1%
1629.08 1
< 0.1%
1578.38 1
< 0.1%
1457.82 1
< 0.1%
1444.63 1
< 0.1%
1426.03 1
< 0.1%
1375.4 1
< 0.1%

prıce
Real number (ℝ)

Distinct9086
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599.14566
Minimum-99.21
Maximum3255.64
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)0.1%
Memory size146.5 KiB
2023-02-14T14:21:45.338587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-99.21
5-th percentile147.37
Q1346.9375
median620.515
Q3799.69
95-th percentile1050.6475
Maximum3255.64
Range3354.85
Interquartile range (IQR)452.7525

Descriptive statistics

Standard deviation303.05796
Coefficient of variation (CV)0.50581682
Kurtosis1.4878463
Mean599.14566
Median Absolute Deviation (MAD)214.035
Skewness0.5422043
Sum5617589.7
Variance91844.125
MonotonicityNot monotonic
2023-02-14T14:21:45.416981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500.12 6
 
0.1%
396.04 4
 
< 0.1%
693.38 4
 
< 0.1%
475 4
 
< 0.1%
214 4
 
< 0.1%
749.1 4
 
< 0.1%
163 3
 
< 0.1%
321.12 3
 
< 0.1%
279.43 3
 
< 0.1%
582.14 3
 
< 0.1%
Other values (9076) 9338
99.6%
ValueCountFrequency (%)
-99.21 1
< 0.1%
-84.51 1
< 0.1%
-76.65 1
< 0.1%
-68.46 1
< 0.1%
-42.83 1
< 0.1%
-41.79 1
< 0.1%
-20.76 1
< 0.1%
6.11 1
< 0.1%
17.11 1
< 0.1%
22.75 1
< 0.1%
ValueCountFrequency (%)
3255.64 1
< 0.1%
2625.04 1
< 0.1%
2339.14 1
< 0.1%
2332.26 1
< 0.1%
2281.56 1
< 0.1%
2161 1
< 0.1%
2147.81 1
< 0.1%
2129.21 1
< 0.1%
2050.97 1
< 0.1%
2035.76 1
< 0.1%

day_convert
Categorical

Distinct776
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size146.5 KiB
2022-10-06
 
38
2022-09-18
 
38
2022-09-26
 
38
2022-09-29
 
38
2022-09-30
 
38
Other values (771)
9186 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters93760
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)1.6%

Sample

1st row2020-01-02
2nd row2020-01-09
3rd row2020-01-02
4th row2020-01-10
5th row2020-03-15

Common Values

ValueCountFrequency (%)
2022-10-06 38
 
0.4%
2022-09-18 38
 
0.4%
2022-09-26 38
 
0.4%
2022-09-29 38
 
0.4%
2022-09-30 38
 
0.4%
2022-10-07 38
 
0.4%
2022-10-13 38
 
0.4%
2022-09-22 38
 
0.4%
2022-09-19 38
 
0.4%
2022-09-25 38
 
0.4%
Other values (766) 8996
95.9%

Length

2023-02-14T14:21:45.500544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-10-06 38
 
0.4%
2022-10-13 38
 
0.4%
2022-09-15 38
 
0.4%
2022-09-25 38
 
0.4%
2022-09-19 38
 
0.4%
2022-09-22 38
 
0.4%
2022-09-18 38
 
0.4%
2022-10-07 38
 
0.4%
2022-09-30 38
 
0.4%
2022-09-29 38
 
0.4%
Other values (766) 8996
95.9%

Most occurring characters

ValueCountFrequency (%)
2 29459
31.4%
0 22803
24.3%
- 18752
20.0%
1 9031
 
9.6%
9 2737
 
2.9%
8 2592
 
2.8%
7 2362
 
2.5%
6 1914
 
2.0%
5 1547
 
1.6%
3 1511
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75008
80.0%
Dash Punctuation 18752
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29459
39.3%
0 22803
30.4%
1 9031
 
12.0%
9 2737
 
3.6%
8 2592
 
3.5%
7 2362
 
3.1%
6 1914
 
2.6%
5 1547
 
2.1%
3 1511
 
2.0%
4 1052
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 18752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29459
31.4%
0 22803
24.3%
- 18752
20.0%
1 9031
 
9.6%
9 2737
 
2.9%
8 2592
 
2.8%
7 2362
 
2.5%
6 1914
 
2.0%
5 1547
 
1.6%
3 1511
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29459
31.4%
0 22803
24.3%
- 18752
20.0%
1 9031
 
9.6%
9 2737
 
2.9%
8 2592
 
2.8%
7 2362
 
2.5%
6 1914
 
2.0%
5 1547
 
1.6%
3 1511
 
1.6%

Interactions

2023-02-14T14:21:39.121583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.029051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.411168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.662416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:29.387755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.582659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.526927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.615114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.721831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.751240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.822708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.011815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.134793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:39.227787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.107147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.493575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.752414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:29.469697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.665377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.611625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.698883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.811767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.840205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.949996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.085922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.200638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:39.291491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.207074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.583395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.831341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:29.600408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.760533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.682093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.779309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.885300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.942257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.018616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.178438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.266766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:39.395531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.281314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.680008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.915901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:29.718262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.829264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.762648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.846018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.962428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.014764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.085368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.298827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.330529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:39.540700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.362658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.757081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:28.020211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:29.883842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.901088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.841311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.945422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.029149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.082496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.172494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.375777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.393625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:39.670999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.439940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.891161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:28.099660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:29.981889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.966532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.915200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.018184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.106001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.155692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.261844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.466453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.460469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:39.784052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.516228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.982006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:28.167145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.063769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.032921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.990108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.086922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.175882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.232498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.331084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.542398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.525167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:39.855707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.687123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.074869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:28.236526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.133372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.095331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.071914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.149247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.248891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.305985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.415324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.608912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.592044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:39.943875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.797360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.150402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:28.307032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.202187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.162691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.171736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.223884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.326762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.376350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.522967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.671034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.732451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:40.037371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:25.899443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.226917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:28.368287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.272147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.224332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.232304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.318561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.400624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.464697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.660980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.760127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.813711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:40.108683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.021117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.289847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:28.435719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.363834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.295348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.326907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.411342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.485462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.536590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.772666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.825202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.886315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:40.177860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.109673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.360296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:28.502491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.428216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.363216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.458551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.498270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.578363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.605387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.841901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:37.893743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.982736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:40.244128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:26.316471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:27.451473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:29.304732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:30.500946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:31.443089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:32.534918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:33.580111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:34.663519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:35.721407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:36.919692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:38.035599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-14T14:21:39.053626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-02-14T14:21:40.378230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-14T14:21:41.622591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-14T14:21:41.876240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DestinationOriginTo AreaFlight Dateday_nameflight_monthseasonyearFlight CodeDaysAirline CompanydptBlockSoldLeftOcc.(%)dpt1Block1Sold1Left1Occ.(%)1Occ.NettoNetto CurrencyProfitprıceday_convert
0TurkeyBelgorodAntalya02.01.2020ThursdayJanuaryWinter2020WZ 4035.4Red Wings Airlinesafternoon2202101095morning220151696995.45177.0EUR61.73238.732020-01-02
1TurkeyBelgorodAntalya09.01.2020ThursdayJanuaryWinter2020WZ 4035.4Red Wings Airlinesafternoon22002200morning22022001000.00174.0EURNaN174.002020-01-09
2TurkeyChelyabinskAntalya02.01.2020ThursdayJanuaryWinter2020U6 10094Ural Airlinesnight220215598morning220181398297.73253.0EUR58.35311.352020-01-02
3TurkeyChelyabinskAntalya10.01.2020FridayJanuaryWinter2020U6 10095Ural Airlinesnight22002200morning2202173990.00236.0EURNaN236.002020-01-10
4TurkeyChelyabinskAntalya15.03.2020SundayMarchSpring2020WZ 40097Red Wings Airlinesafternoon220218299morning2201219099.09251.0EUR-50.69200.312020-03-15
5TurkeyChelyabinskAntalya22.03.2020SundayMarchSpring2020WZ 40097Red Wings Airlinesafternoon22002200morning22021911000.00252.0EURNaN252.002020-03-22
6TurkeyVolgogradAntalya06.01.2020MondayJanuaryWinter2020WZ 40211Red Wings Airlinesafternoon22002200morning2202146970.00174.0EURNaN174.002020-01-06
7TurkeyVoronezhAntalya07.01.2020TuesdayJanuaryWinter2020WZ 40412Red Wings Airlinesafternoon22002200morning22022001000.00175.0EURNaN175.002020-01-07
8TurkeyMoscowAntalya10.11.2020TuesdayNovemberFall2020U6 30012Ural Airlinesnight2206615430morning220220010030.00148.0EUR-20.01127.992020-11-10
9TurkeyMoscowAntalya10.11.2020TuesdayNovemberFall2020RL 77032Royal Flightnight3306726320afternoon330330010020.30144.0EUR-21.77122.232020-11-10
DestinationOriginTo AreaFlight Dateday_nameflight_monthseasonyearFlight CodeDaysAirline CompanydptBlockSoldLeftOcc.(%)dpt1Block1Sold1Left1Occ.(%)1Occ.NettoNetto CurrencyProfitprıceday_convert
6512TurkeyS.PetersburgAntalya15.07.2022FridayJulySummer2022TK 36735Turkish Airlines02:45:006565010010:00:0065650100100.0377.18EUR419.47796.652022-07-15
6513TurkeyS.PetersburgAntalya15.07.2022FridayJulySummer2022TK 37165Turkish Airlines15:30:006464010020:55:0064640100100.0377.18EUR553.40930.582022-07-15
6514TurkeyS.PetersburgAntalya15.07.2022FridayJulySummer2022TK 39615Turkish Airlines12:55:004646010007:25:0046460100100.0343.18EUR487.99831.172022-07-15
6515TurkeyS.PetersburgAntalya16.07.2022SaturdayJulySummer2022TK 36576Turkish Airlines13:35:005454010020:05:0054540100100.0377.18EUR480.39857.572022-07-16
6516TurkeyS.PetersburgAntalya16.07.2022SaturdayJulySummer2022TK 36576Turkish Airlines13:35:0055010020:05:00550100100.0703.18EUR697.931401.112022-07-16
6517TurkeyS.PetersburgAntalya16.07.2022SaturdayJulySummer2022TK 36736Turkish Airlines02:30:006565010009:25:0065650100100.0377.18EUR420.95798.132022-07-16
6518TurkeyS.PetersburgAntalya16.07.2022SaturdayJulySummer2022TK 39616Turkish Airlines12:55:004646010007:25:0046460100100.0343.18EUR398.44741.622022-07-16
6519TurkeyS.PetersburgAntalya17.07.2022SundayJulySummer2022TK 12347Turkish Airlines13:05:003434010007:45:0034340100100.0370.00USD512.82882.822022-07-17
6520TurkeyS.PetersburgAntalya17.07.2022SundayJulySummer2022TK 36577Turkish Airlines13:35:005454010020:05:0054540100100.0377.18EUR467.51844.692022-07-17
6521TurkeyS.PetersburgAntalya17.07.2022SundayJulySummer2022TK 36577Turkish Airlines13:35:0055010020:05:00550100100.0703.18EUR361.371064.552022-07-17